Abstract
Many real-world problems can be abstracted into graph classification problems. Recently, graph convolutional networks have achieved great success in the task of node classification and link prediction. However, when using graph convolution network to process the task of graph classification, either global topology information or local information is ignored. Therefore, designing graph convolutional networks to improve the accuracy of graph classification has attracted more and more attention. Inspired by the use of convolutional neural networks to process graph-structured data, we put forward a new spatial convolutional neural network architecture for graph classification. To be specific, we first design a comprehensive weighting method to measure the significance of vertices in the graph based on multiple indicators to choose the central node sequence. Then, the normalization process of the graph is realized by constructing the same size neighborhood graphs for the central vertices. After that, the structural characteristics of the graph are extracted from both local and global aspects. Finally, the tensors obtained after the above steps are respectively input into the following two spatial convolutional neural network architectures to perform classification, one is a simple CNN structure, which has only two convolution layers, one dense layer and one softmax layer. The other is to modify the architecture of CNN, and the channel concatenation layer is introduced to determine the classification result of the entire graph according to the category of the neighborhood graphs. Experimental results on two kinds of real-world datasets, bioinformatics and social network datasets, indicate that our approach obtains competitive results and is superior to some classic kernels and similar deep learning-based algorithms on 6 out of 8 benchmark data sets. (c) 2020 Elsevier B.V. All rights reserved.